Quality Of Light Field Research Articles

Analysis of the hybrid light field reconstruction and comparison with Richardson-Lucy Light Field Deconvolution

Conventional microscopes have a high spatial resolution and a low depth-of-field. Light field microscopes have a high depth-of-field but low spatial resolution. A new hybrid approach uses information from both systems to reconstruct a high-resolution light field [Appl. Opt. 58, A142 (2019)APOPAI0003-693510.1364/AO.58.00A142]. The resolution of the resulting light field is said to be limited only by diffraction and the size of the pixels. In this paper, we evaluate this method. Using simulation data we compare the output of the hybrid reconstruction algorithm with its simulated ground truth. Our analyses reveal that the observed improvement in the light field quality is not a consequence of data fusion or incorporation of information from a conventional camera, but rather the results of an intermediate interpolation step within the light field itself. This suggests that the required information is already inherent to the light field. By employing the Richardson-Lucy Light Field Deconvolution algorithm, we demonstrate that existing algorithms have already utilized this information.

Light field quality assessment based on aggregation learning of multiple visual features.

Light field imaging is a way to represent human vision from a computational perspective. It contains more visual information than traditional imaging systems. As a basic problem of light field imaging, light field quality assessment has received extensive attention in recent years. In this study, we explore the characteristics of light field data for different visual domains (spatial, angular, coupled, projection, and depth), study the multiple visual features of a light field, and propose a non-reference light field quality assessment method based on aggregation learning of multiple visual features. The proposed method has four key modules: multi-visual representation of a light field, feature extraction, feature aggregation, and quality assessment. It first extracts the natural scene statistics (NSS) features from the central view image in the spatial domain. It extracts gray-level co-occurrence matrix (GLCM) features both in the angular domain and in the spatial-angular coupled domain. Then, it extracts the rotation-invariant uniform local binary pattern (LBP) features of depth map in the depth domain, and the statistical characteristics of the local entropy (SDLE) features of refocused images in the projection domain. Finally, the multiple visual features are aggregated to form a visual feature vector for the light field. A prediction model is trained by support vector machines (SVM) to establish a light field quality assessment method based on aggregation learning of multiple visual features.

Beyond Perceptual Thresholds and Personal Preference: Towards Novel Research Questions and Methodologies of Quality of Experience Studies on Light Field Visualization

With the upcoming emergence of the IEEE P3333.1.4 standard, the first ever standardization document on the subjective quality assessment of light field visualization, we are approaching a major milestone of light field Quality of Experience (QoE). The research efforts leading up to this point have answered the fundamental questions regarding perceptual thresholds and personal preference, and best practices regarding research methodologies have been formed. However, in order to introduce successful and efficient light field systems and services, research questions beyond perceptual thresholds and personal preference must be addressed, along with the associated methodologies. In this paper, we propose a comprehensive set of novel research questions and methodologies regarding light field QoE. Our work also provides a detailed discussion of related factors, particularly those that extend to test participants and thus may affect the results of subjective studies.

Objective Quality Assessment Metrics for Light Field Image Based on Textural Features

Light Field (LF) imaging is a plenoptic data collection method enabling a wide variety of image post-processing such as 3D extraction, viewpoint change and digital refocusing. Moreover, LF provides the capability to capture rich information about a scene, e.g., texture, geometric information, etc. Therefore, a quality assessment model for LF images is needed and poses significant challenges. Many LF Image Quality Assessment (LF-IQA) metrics have been recently presented based on the unique characteristics of LF images. The state-of-the-art objective assessment metrics have taken into account the image content and human visual system such as SSIM and IW-SSIM. However, most of these metrics are designed for images and video with natural content. Additionally, other models based on the LF characteristics (e.g., depth information, angle information) trade high performance for high computational complexity, along with them possessing difficulties of implementation for LF applications due to the immense data requirements of LF images. Hence, this paper presents a novel content-adaptive LF-IQA metric to improve the conventional LF-IQA performance that is also low in computational complexity. The experimental results clearly show improved performance compared to conventional objective IQA metrics, and we also identify metrics that are well-suited for LF image assessment. In addition, we present a comprehensive content-based feature analysis to determine the most appropriate feature that influences human visual perception among the widely used conventional objective IQA metrics. Finally, a rich LF dataset is selected from the EPFL dataset, allowing for the study of light field quality by qualitative factors such as depth (wide and narrow), focus (background or foreground) and complexity (simple and complex).

Objective Quality Assessment of Lenslet Light Field Image Based on Focus Stack

The large amount of complex scene information recorded by light field imaging has the potential for immersive media applications. Compression and reconstruction algorithms are crucial for the transmission, storage, and display of such massive data. Most of the existing quality evaluation indexes do not effectively account for light field characteristics. To accurately evaluate the distortions caused by compression and reconstruction algorithms, it is necessary to construct an image evaluation index that reflects the angular-spatial characteristics of the light field. This work proposes a full-reference light field image quality evaluation index that attempts to extract less information from the focus stack to accurately evaluate the entire light field quality. The proposed framework includes three specific steps. First, we construct a key refocused image extraction framework by the maximal spatial information contrast and the minimal angular information variation. Specifically, the gradient and phase congruency operators are used in the extraction framework. Second, a novel light field quality evaluation index is built based on the angular-spatial characteristics of the key refocused images. In detail, the features used in the key refocused image extraction framework and the chrominance feature are combined to construct the union feature. Third, the similarity of the union feature is pooled by the relevant visual saliency map to obtain the predicted score. Finally, the overall quality of the light field is measured by applying the proposed index to the key refocused images. The high efficiency and precision of the proposed method are shown by extensive comparison experiments.

An Occlusion Compensation Learning Framework for Improving the Rendering Quality of Light Field.

Occlusions are common phenomena in light field rendering (LFR) technology applications. The 3-D spatial structures of some features may be missing or incorrect when capturing some samples due to occlusion discontinuities. Most prior works on LFR, however, have neglected occlusions from other objects in 3-D scenes that do not participate in the capturing and rendering of the light field. To improve rendering quality, this report proposes an occlusion probability learning framework (OPLF) based on a deep Boltzmann machine (DBM) to compensate for the occluded information. In the OPLF, an occlusion probability density model is applied to calculate the visibility scores, which are modeled as hidden variables. Additionally, the probability of occlusion is related to the visibility, the camera configuration (i.e., position and direction), and the relationship between the occlusion object and occluded object. Furthermore, a deep probability model based on the OPLF is used for learning the occlusion relationship between the camera and object in multiple layers. The proposed OPLF can optimize the LFR quality. Finally, to verify the claimed performance, we also compare the OPLF with the most advanced occlusion theory and light field reconstruction algorithms. The experimental results show that the proposed OPLF outperforms other known occlusion quantization schemes.

A Metric for Light Field Reconstruction, Compression, and Display Quality Evaluation.

Owning to the recorded light ray distributions, light field contains much richer information and provides possibilities of some enlightening applications, and it has becoming more and more popular. To facilitate the relevant applications, many light field processing techniques have been proposed recently. These operations also bring the loss of visual quality, and thus there is need of a light field quality metric to quantify the visual quality loss. To reduce the processing complexity and resource consumption, light fields are generally sparsely sampled, compressed, and finally reconstructed and displayed to the users. We consider the distortions introduced in this typical light field processing chain, and propose a full-reference light field quality metric. Specifically, we measure the light field quality from three aspects: global spatial quality based on view structure matching, local spatial quality based on near-edge mean square error, and angular quality based on multi-view quality analysis. These three aspects have captured the most common distortions introduced in light field processing, including global distortions like blur and blocking, local geometric distortions like ghosting and stretching, and angular distortions like flickering and sampling. Experimental results show that the proposed method can estimate light field quality accurately, and it outperforms the state-of-the-art quality metrics which may be effective for light field.

Modeling and control of color tunable lighting systems

Electric lighting has not substantially changed in over 100 years. From incandescent bulbs to fluorescent tubes, the efficiency remains low and control mostly involves on/off or dimming. The new wave of solid state lighting offers the possibility of sensor-based intensity regulation, color control, and energy efficiency, under varying needs and environmental conditions. This paper formulates the lighting control problem as an optimization problem balancing color fidelity, human perception and comfort, light field uniformity, and energy efficiency. The optimization problem is solved based on the light propagation model, which is adaptively updated with color sensor feedback to account for changing ambient lighting conditions, such as daylighting. We demonstrate the proposed approach in a smart space testbed under a variety of use conditions. The testbed is instrumented with 12 color tunable lights and 12 light sensors, as well as simulated daylight. The results show substantial improvement in terms of energy usage and delivering good light field quality in the presence of varying lighting conditions. Experimental results corroborate the efficacy of the proposed algorithms.

Design and Fabrication of Faceted Mirror Arrays for Light Field Capture

AbstractThe high resolution of digital cameras has made single‐shot, single‐sensor acquisition of light fields feasible, though considerable design effort is still necessary in order to construct the necessary collection of optical elements for particular acquisition scenarios. This paper explores a pipeline for designing, fabricating and utilizing faceted mirror arrays which simplifies this task. The foundation of the pipeline is an interactive tool that automatically optimizes for mirror designs while exposing to the user a set of intuitive parameters for light field quality and manufacturing constraints. We investigate two manufacturing processes for automatic fabrication of the resulting designs: one is based on CNC milling, polishing, and plating of one solid work piece, while the other involves assembly of CNC‐cut mirror facets. We demonstrate results for refocusing in a macro photography scenario. In addition, we observe that traditional photographic parameters take novel roles in the faceted mirror array setup and discuss their influence.

SU‐FF‐T‐201: Implementation of EPID Based Quality Assurance Procedures for Radiation and Optical Fields

Purpose: Enhanced radiation and light field quality assurance (QA) procedures have been developed and implemented utilizing amorphous silicon electronic portal imaging devices (EPID). This effort was motivated by the fact that film exposure coupled with hand measurements are extremely time consuming and prone to many sources of error. Method and Materials: The EPID based QA procedure uses specially designed acrylic test templates, containing radio‐opaque led bars to register the position of the optical field. Images of these templates were acquired with the EPID on both Varian and Elekta linear accelerators. A MATLab based computer application was used to automatically assess light/radiation field congruence, radiation field sizes and optical field size and positioning. A three‐month pilot study using two Varian accelerators to assess both 6 MV and 18 MV photon energies was performed to validate the EPID QA procedure's efficacy and quantify its efficiency relative to film QA procedures. Results: The pilot study verified that the EPID QA system results are congruent with the hand measurements obtained from a detailed analysis of film QA measurements. Light/radiation congruence measurement performed using an EPID exhibited an absolute mean deviation of 0.37 ± 0.37 mm (1σ), relative to the film measurements. EPID based radiation field size assessments produced an absolute discrepancy of 0.57 ± 0.44mm (1σ), while film measurements yielded an absolute discrepancy of 0.61 ± 0.55mm (1σ), relative to measurements acquired using RIT software. EPID based measurements were found to be more efficiently performed, reducing the set‐up and analysis time by 55% compared to film. Conclusions: The enhanced EPID QA system can effectively replace film in routine quality assurance tasks for radiation and optical fields. This procedure saves Princess Margaret Hospital 4 man‐hours per week, and provides a user‐independent analysis procedure for objectively assessing routine QA results.

Evaluating the quality of light fields computed from hand-held camera images

Given an image sequence recorded by a hand-held camera we examine the computation of a light field without any further input data. Using structure-from-motion algorithms and optimization techniques camera motion and a 3-D reconstruction of the scene are established. The light field is completed by computing local depth information for each input image. During experimental evaluation a special focus is set on the effects of falsely estimated intrinsic parameters as well as different depth representations on the quality of the resulting light fields.